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Efficient and Private Federated Learning with Partially Trainable Networks


Oct 06, 2021
Hakim Sidahmed, Zheng Xu, Ankush Garg, Yuan Cao, Mingqing Chen


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A Field Guide to Federated Optimization


Jul 14, 2021
Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Aguera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horvath, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecny, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtarik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu


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Local Adaptivity in Federated Learning: Convergence and Consistency


Jun 04, 2021
Jianyu Wang, Zheng Xu, Zachary Garrett, Zachary Charles, Luyang Liu, Gauri Joshi


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Practical and Private (Deep) Learning without Sampling or Shuffling


Feb 26, 2021
Peter Kairouz, Brendan McMahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, Zheng Xu


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GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training


Feb 16, 2021
Chen Zhu, Renkun Ni, Zheng Xu, Kezhi Kong, W. Ronny Huang, Tom Goldstein


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Towards Accurate Quantization and Pruning via Data-free Knowledge Transfer


Oct 14, 2020
Chen Zhu, Zheng Xu, Ali Shafahi, Manli Shu, Amin Ghiasi, Tom Goldstein


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Exploring Model Robustness with Adaptive Networks and Improved Adversarial Training


May 30, 2020
Zheng Xu, Ali Shafahi, Tom Goldstein


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Advances and Open Problems in Federated Learning


Dec 10, 2019
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konečný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao


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Adversarial Training for Free!


Apr 29, 2019
Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S. Davis, Gavin Taylor, Tom Goldstein


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The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent


Apr 15, 2019
Karthik A. Sankararaman, Soham De, Zheng Xu, W. Ronny Huang, Tom Goldstein


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Universal Adversarial Training


Nov 27, 2018
Ali Shafahi, Mahyar Najibi, Zheng Xu, John Dickerson, Larry S. Davis, Tom Goldstein


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Weakly Supervised Deep Learning for Thoracic Disease Classification and Localization on Chest X-rays


Jul 16, 2018
Chaochao Yan, Jiawen Yao, Ruoyu Li, Zheng Xu, Junzhou Huang

* 10 pages. Accepted by the ACM BCB 2018 

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Accurate and efficient video de-fencing using convolutional neural networks and temporal information


Jun 28, 2018
Chen Du, Byeongkeun Kang, Zheng Xu, Ji Dai, Truong Nguyen


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Beyond Textures: Learning from Multi-domain Artistic Images for Arbitrary Style Transfer


May 25, 2018
Zheng Xu, Michael Wilber, Chen Fang, Aaron Hertzmann, Hailin Jin


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The Effectiveness of Instance Normalization: a Strong Baseline for Single Image Dehazing


May 08, 2018
Zheng Xu, Xitong Yang, Xue Li, Xiaoshuai Sun


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Learning to Cluster for Proposal-Free Instance Segmentation


Mar 17, 2018
Yen-Chang Hsu, Zheng Xu, Zsolt Kira, Jiawei Huang


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Visualizing the Loss Landscape of Neural Nets


Mar 05, 2018
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein


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Stabilizing Adversarial Nets With Prediction Methods


Feb 08, 2018
Abhay Yadav, Sohil Shah, Zheng Xu, David Jacobs, Tom Goldstein

* Accepted at ICLR 2018 

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Training Quantized Nets: A Deeper Understanding


Nov 13, 2017
Hao Li, Soham De, Zheng Xu, Christoph Studer, Hanan Samet, Tom Goldstein

* NIPS 2017 

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Group-driven Reinforcement Learning for Personalized mHealth Intervention


Aug 14, 2017
Feiyun Zhu, Jun Guo, Zheng Xu, Peng Liao, Junzhou Huang


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Adaptive ADMM with Spectral Penalty Parameter Selection


Jul 19, 2017
Zheng Xu, Mario A. T. Figueiredo, Tom Goldstein

* AISTATS 2017 

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Adaptive Consensus ADMM for Distributed Optimization


Jun 20, 2017
Zheng Xu, Gavin Taylor, Hao Li, Mario Figueiredo, Xiaoming Yuan, Tom Goldstein

* ICML 2017 

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Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation


Apr 10, 2017
Zheng Xu, Mario A. T. Figueiredo, Xiaoming Yuan, Christoph Studer, Tom Goldstein

* CVPR 2017 

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Non-negative Factorization of the Occurrence Tensor from Financial Contracts


Dec 10, 2016
Zheng Xu, Furong Huang, Louiqa Raschid, Tom Goldstein

* NIPS tensor workshop 

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An Empirical Study of ADMM for Nonconvex Problems


Dec 10, 2016
Zheng Xu, Soham De, Mario Figueiredo, Christoph Studer, Tom Goldstein

* NIPS nonconvex workshop 

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Exploiting Lists of Names for Named Entity Identification of Financial Institutions from Unstructured Documents


Jun 07, 2016
Zheng Xu, Douglas Burdick, Louiqa Raschid


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Training Neural Networks Without Gradients: A Scalable ADMM Approach


May 06, 2016
Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit Patel, Tom Goldstein


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